Accelerometers support the farmer with collecting information about animal behavior and thus allow a reduction in visual observation time. The milk intake of calves fed by teat-buckets has not been monitored automatically on commercial farms so far, although it is crucial for the calves’ development. This pilot study was based on bucket-fed dairy calves and intended (1) to evaluate the technical feasibility of using an ear-attached accelerometer (SMARTBOW, Smartbow GmbH, Weibern, Austria) to identify drinking events, (2) to develop an algorithm to detect milk intake, and (3) to validate the SMARTBOW sensor incorporating the algorithm developed under (2) for identifying drinking events against observations from video. The acceleration data used in this study were generated from three sensors attached to the ears of three preweaned calves. Sensor data were recorded for 5  d for 24 h/d and calf behavior was video camera-recorded during the same time period . Based on a training data set, an algorithm was developed to identify drinking events. In addition, a mathematical data simulation was performed which generated further 15  d of data. The complete data set was compared with video recordings to analyze whether drinking events (n = 174) were detected correctly. Sensitivity (82.9 %), specificity (96.9 %), and accuracy (96.2 %) were good, but precision (60.4 %) was not yet satisfactory. Cohen’s kappa (0.68) indicated a substantial agreement between sensor and video analysis. Additional work with a larger number of animals is planned to further improve the algorithm.